code: def vis_traditional(
      self,
      feature_list=None,
      *,
      transforms=[transform.jitter(2)],
      l2_coeff=0.0,
      l2_layer_name=None,
  ):
      if feature_list is None:
          feature_list = list(range(self.acts_reduced.shape[-1]))
      try:
          feature_list = list(feature_list)
      except TypeError:
          feature_list = [feature_list]
  
      obj = sum(
          [
              objectives.direction_neuron(
                  self.layer_name,
                  self.channel_dirs[feature],
                  batch=feature,
              )
              for feature in feature_list
          ]
      )
      if l2_coeff != 0.0:
          assert (
              l2_layer_name is not None
          ), "l2_layer_name must be specified if l2_coeff is non-zero"
          obj -= objectives.L2(l2_layer_name) * l2_coeff
      param_f = lambda: param.image(64, batch=len(feature_list))
      return render.render_vis(
          self.model, obj, param_f=param_f, transforms=transforms
      )[-1]
  
file_path: lucid/lucid/scratch/rl_util/nmf.py
imports: import numpy
  from functools import reduce
  import scipy.ndimage
  from lucid.misc.channel_reducer import ChannelReducer
  import lucid.optvis.param
  import lucid.optvis.objectives
  import lucid.optvis.render
  import lucid.optvis.transform
  from .attribution import get_acts, get_attr
label_desc: Use image node_id 2 for calling the LayerNMF obj's underlying member method code with example usage: obj.vis_traditional(feature_list) and returns: render